Determinants of clinical response to empirical antibiotic treatment in patients with cirrhosis and bacterial and fungal infections—Results from the ICA “Global Study” (EABCIR-Global Study)
Bibliographic record
Abstract
BACKGROUND: The administration of an appropriate empirical antibiotic treatment is essential in cirrhosis and severe bacterial infections. We aimed to investigate the predictors of clinical response of empirical antibiotic treatment in a prospective cohort of patients with cirrhosis and bacterial and fungal infections included in the International Club of Ascites "Global Study." METHODS: Patients hospitalized with cirrhosis and bacterial/fungal infection were prospectively enrolled at 46 centers. Clinical response to antibiotic treatment was defined according to changes in markers of infection/inflammation, vital signs, improvement of organ failure, and results of cultures. RESULTS: From October 2015 to September 2016, 1302 patients were included at 46 centers. A clinical response was achieved in only 61% of cases. Independent predictors of lack of clinical response to empirical treatment were C-reactive protein (OR = 1.16; 95% CI = 1.02-1.31), blood leukocyte count (OR = 1.39;95% CI = 1.09-1.77), serum albumin (OR = 0.70; 95% CI = 0.55-0.88), nosocomial infections (OR = 1.96; 95% CI = 1.20-2.38), pneumonia (OR = 1.75; 95% CI = 1.22-2.53), and ineffective treatment according to antibiotic susceptibility test (OR = 5.32; 95% CI = 3.47-8.57). Patients with a lack of clinical response to first-line antibiotic treatment had a significantly lower resolution rate of infections (55% vs. 96%; p < 0.001), a higher incidence of second infections (29% vs. 15%; p < 0.001), shock (35% vs. 7%; p < 0.001) and new organ failures (52% vs. 19 %; p < 0.001) than responders. Clinical response to empirical treatment was an independent predictor of 28-day survival ( subdistribution = 0.20; 95% CI = 0.14-0.27). CONCLUSIONS: Four out of 10 patients with cirrhosis do not respond to the first-line antibiotic therapy, leading to lower resolution of infections and higher mortality. Broader-spectrum antibiotics and strategies targeting systemic inflammation may improve prognosis in patients with a high degree of inflammation, low serum albumin levels, and severe liver impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".